提出神经符号AI的统一形式定义，融合逻辑与信念函数推理。
Defining neurosymbolic AI
- 将神经符号推理定义为逻辑函数与信念函数乘积的积分计算
- 该定义可涵盖多个代表性神经符号系统，具有通用抽象性
- 适合对逻辑推理与深度学习融合感兴趣的研究者
神经符号AI致力于整合学习与推理，特别是统一逻辑表示与神经表示。尽管已有多种神经符号AI系统，但该领域缺乏关于神经符号模型与推理的公认正式定义。本文提出一个形式化定义：神经符号推理是逻辑函数与信念函数乘积在全域上的积分计算。我们证明该定义能抽象出多个关键代表性的神经符号系统，具备广泛适用性。
原文摘要 · Abstract (English)
Neurosymbolic AI focuses on integrating learning and reasoning, in particular, on unifying logical and neural representations. Despite the existence of an alphabet soup of neurosymbolic AI systems, the field is lacking a generally accepted formal definition of what neurosymbolic models and inference really are. We introduce a formal definition for neurosymbolic AI that makes abstraction of its key ingredients. More specifically, we define neurosymbolic inference as the computation of an integral over a product of a logical and a belief function. We show that our neurosymbolic AI definition makes abstraction of key representative neurosymbolic AI systems.
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